Can RTX 4070 Ti run Nomic Embed Text v1.5?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 Ti (12 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 132 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. High quality text embedding model. 137M params. Good for RAG and search.
Setup tutorial: Nomic Embed Text v1.5 on RTX 4070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Nomic Embed Text v1.5 runs at Grade S on an NVIDIA GeForce RTX 4070 Ti with FP16 quantization, achieving ~716 tok/sec.
Prerequisites
Before starting, ensure you have at least 0.3GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 or later installed.
Expected performance
With the FP16 quantization, you can expect the model to run at approximately 716 tokens per second, using around 0.8GB of VRAM. This leaves about 11.2GB of VRAM available for the context window, allowing for a practical context length of up to 8192 tokens without running into memory constraints.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the FP16 quantized model (0.3GB) from Hugging Face.
ollama pull nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf3. Run it
ollama run nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf --device cuda
ollama interact nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf4. Optimize for RTX 4070 Ti
For optimal performance on the NVIDIA GeForce RTX 4070 Ti with 12GB VRAM, set --n-gpu-layers to 137 (the total number of layers) to fully utilize the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Given the 12GB VRAM, you can comfortably run the model with a context length of 8192 tokens, leaving ample headroom for other tasks.
Troubleshooting
Out of memory error during inference
Reduce the context length or try setting --n-gpu-layers to a lower value to decrease VRAM usage.
Slow inference speed
Ensure that CUDA is properly installed and that the --device cuda flag is used. Also, enable flash attention with --flash-attn to optimize performance.
Model not found
Verify that the model was successfully downloaded and is available in the Ollama model directory. Use `ollama list` to check available models.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used to run Nomic Embed Text v1.5. LM Studio is suitable for a more user-friendly interface, while llama.cpp offers more control over low-level optimizations. Jan is a good choice for cloud-based deployments. However, Ollama provides a balanced approach with ease of use and good performance, making it the recommended choice for the NVIDIA GeForce RTX 4070 Ti.
Other models that run great on RTX 4070 Ti
FAQ (20)
What GPU do I need to run Nomic Embed Text v1.5?
Nomic Embed Text v1.5 requires a GPU with at least 0.3 GB of VRAM for basic operation, but 0.8 GB is recommended for optimal performance, especially with higher quantization levels.
Is Nomic Embed Text v1.5 good for coding?
Nomic Embed Text v1.5 is primarily designed for text embedding tasks, which can be useful for code search and retrieval, but it may not be as specialized for coding as models specifically trained on programming data.
Nomic Embed Text v1.5 vs Llama 3.1 8B?
Nomic Embed Text v1.5 has 0.137 billion parameters, making it significantly smaller than Llama 3.1 8B, which has 8 billion parameters. This makes Nomic Embed Text v1.5 more lightweight and easier to run on less powerful hardware, but it may not perform as well on complex tasks.
Can I run Nomic Embed Text v1.5 on a Mac?
Yes, you can run Nomic Embed Text v1.5 on a Mac, provided your Mac meets the minimum VRAM requirements of 0.3 GB and has the necessary software dependencies installed.
How much VRAM does Nomic Embed Text v1.5 need?
Nomic Embed Text v1.5 requires between 0.3 GB and 0.8 GB of VRAM, depending on the quantization level used. Lower quantization levels require less VRAM but may impact performance.
Is Nomic Embed Text v1.5 censored?
Nomic Embed Text v1.5 is not explicitly censored. However, it adheres to ethical guidelines and best practices in AI development, which may influence its training data and output.
Is Nomic Embed Text v1.5 commercial-use allowed?
Yes, Nomic Embed Text v1.5 is licensed under the Apache-2.0 license, which allows for both commercial and non-commercial use without restriction.
Nomic Embed Text v1.5 context length?
Nomic Embed Text v1.5 supports a context length of up to 8192 tokens, which is quite generous for most text embedding tasks.
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